Utilizing excitation-emission matrix fluorescence spectroscopy and convolutional neural networks for dark tea brand and aging period identification

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This study utilized excitation-emission matrix (EEM) fluorescence spectroscopy in conjunction with convolutional neural networks (CNNs) to identify the dark tea brands and their aging periods, which were both key factors of market value. The adaptation of EEM data to the CNN model was examined, encompassing the configuration of the data structure and the implementation of data augmentation. Three typical CNN models including AlexNet, GoogLeNet, and ResNet were evaluated. ResNet was selected because it demonstrated the highest classification accuracy on the test set and superior clustering performance in the t-SNE visualization of extracted features. Analysis of feature contribution suggested that the CNN model exhibited enhanced sensitivity towards the boundaries of the fluorescence region. Furthermore, transfer learning was proved to be effective in facilitating adaptation of dark tea recognition models to databases with different sample sizes, which was meaningful for the application of this method in dark tea market supervision. Finally, the multi-task model was developed and achieved a 96.9 % accuracy in brand identification and 87.5 % in aging period determination, suggesting the advantages of multi-task learning in enhancing model generalization and training efficiency.